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Optimizing COVID-19 vaccine distribution across the United States using deterministic and stochastic recurrent neural networks
Optimizing COVID-19 vaccine distribution can help plan around the limited production and distribution of vaccination, particularly in early stages. One of the main criteria for equitable vaccine distribution is predicting the geographic distribution of active virus at the time of vaccination. This r...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8259963/ https://www.ncbi.nlm.nih.gov/pubmed/34228740 http://dx.doi.org/10.1371/journal.pone.0253925 |
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author | Davahli, Mohammad Reza Karwowski, Waldemar Fiok, Krzysztof |
author_facet | Davahli, Mohammad Reza Karwowski, Waldemar Fiok, Krzysztof |
author_sort | Davahli, Mohammad Reza |
collection | PubMed |
description | Optimizing COVID-19 vaccine distribution can help plan around the limited production and distribution of vaccination, particularly in early stages. One of the main criteria for equitable vaccine distribution is predicting the geographic distribution of active virus at the time of vaccination. This research developed sequence-learning models to predict the behavior of the COVID-19 pandemic across the US, based on previously reported information. For this objective, we used two time-series datasets of confirmed COVID-19 cases and COVID-19 effective reproduction numbers from January 22, 2020 to November 26, 2020 for all states in the US. The datasets have 310 time-steps (days) and 50 features (US states). To avoid training the models for all states, we categorized US states on the basis of their similarity to previously reported COVID-19 behavior. For this purpose, we used an unsupervised self-organizing map to categorize all states of the US into four groups on the basis of the similarity of their effective reproduction numbers. After selecting a leading state (the state with earliest outbreaks) in each group, we developed deterministic and stochastic Long Short Term Memory (LSTM) and Mixture Density Network (MDN) models. We trained the models with data from each leading state to make predictions, then compared the models with a baseline linear regression model. We also remove seasonality and trends from a dataset of non-stationary COVID-19 cases to determine the effects on prediction. We showed that the deterministic LSTM model trained on the COVID-19 effective reproduction numbers outperforms other prediction methods. |
format | Online Article Text |
id | pubmed-8259963 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-82599632021-07-19 Optimizing COVID-19 vaccine distribution across the United States using deterministic and stochastic recurrent neural networks Davahli, Mohammad Reza Karwowski, Waldemar Fiok, Krzysztof PLoS One Research Article Optimizing COVID-19 vaccine distribution can help plan around the limited production and distribution of vaccination, particularly in early stages. One of the main criteria for equitable vaccine distribution is predicting the geographic distribution of active virus at the time of vaccination. This research developed sequence-learning models to predict the behavior of the COVID-19 pandemic across the US, based on previously reported information. For this objective, we used two time-series datasets of confirmed COVID-19 cases and COVID-19 effective reproduction numbers from January 22, 2020 to November 26, 2020 for all states in the US. The datasets have 310 time-steps (days) and 50 features (US states). To avoid training the models for all states, we categorized US states on the basis of their similarity to previously reported COVID-19 behavior. For this purpose, we used an unsupervised self-organizing map to categorize all states of the US into four groups on the basis of the similarity of their effective reproduction numbers. After selecting a leading state (the state with earliest outbreaks) in each group, we developed deterministic and stochastic Long Short Term Memory (LSTM) and Mixture Density Network (MDN) models. We trained the models with data from each leading state to make predictions, then compared the models with a baseline linear regression model. We also remove seasonality and trends from a dataset of non-stationary COVID-19 cases to determine the effects on prediction. We showed that the deterministic LSTM model trained on the COVID-19 effective reproduction numbers outperforms other prediction methods. Public Library of Science 2021-07-06 /pmc/articles/PMC8259963/ /pubmed/34228740 http://dx.doi.org/10.1371/journal.pone.0253925 Text en © 2021 Davahli et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Davahli, Mohammad Reza Karwowski, Waldemar Fiok, Krzysztof Optimizing COVID-19 vaccine distribution across the United States using deterministic and stochastic recurrent neural networks |
title | Optimizing COVID-19 vaccine distribution across the United States using deterministic and stochastic recurrent neural networks |
title_full | Optimizing COVID-19 vaccine distribution across the United States using deterministic and stochastic recurrent neural networks |
title_fullStr | Optimizing COVID-19 vaccine distribution across the United States using deterministic and stochastic recurrent neural networks |
title_full_unstemmed | Optimizing COVID-19 vaccine distribution across the United States using deterministic and stochastic recurrent neural networks |
title_short | Optimizing COVID-19 vaccine distribution across the United States using deterministic and stochastic recurrent neural networks |
title_sort | optimizing covid-19 vaccine distribution across the united states using deterministic and stochastic recurrent neural networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8259963/ https://www.ncbi.nlm.nih.gov/pubmed/34228740 http://dx.doi.org/10.1371/journal.pone.0253925 |
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